Land degradation remain a critical environmental challenge worldwide, particularly under accelerating climate change. This study assesses spatial and temporal patterns of land degradation and agriculture change in Shaanxi Province, China from 2014 to 2024 by integrating time-series statistical data with remote sensing (RS) techniques. Time series data were used to quantify the total cropland extent, grain, and major crops (wheat and rice) cultivation areas and fertilizer (NPK) consumption; vegetation health and land use land cover (LULC) categories were evaluated to identify dominant drivers and inform sustainable land-management strategies. The results indicated that total cropland area increased by 5.8%, while wheat and rice cultivation areas decreased by 5.9% and 3.2%, respectively, during the study period. Furthermore, RS data shows NDVI median values improving slightly from 0.39 in 2019 to 0.44 in 2024, suggesting gradual recovery of vegetation cover under changing climatic conditions. LULC results revealed minor yet reliable transformations, with cropland showing a modest increase of 4.7% from 2014 to 2024, indicating agricultural stability, rather than large-scale land conversion. Additionally, NPK fertilizer consumption showed a general decrease, reflecting improved input efficiency. Novelty of this research lies in the synchronized integration of long-term statistical record with multi-temporal RS indicators to jointly quantify land use dynamics, vegetation recovery and fertilizer use efficiency at provincial level. Overall, integrating satellite-based and statistical data provided a comprehensive understanding of agricultural dynamics, highlighting the interrelation between land cover change, vegetation condition, and input management practices in Shaanxi Province.
{"title":"Sustainable Land Management Strategies to Combat Degradation Under Changing Climate Conditions","authors":"Hao Fu, Yuanyuan Zhong, Hao Jian, Gangbiao Xu, Runmei Duan","doi":"10.1002/ldr.70514","DOIUrl":"https://doi.org/10.1002/ldr.70514","url":null,"abstract":"Land degradation remain a critical environmental challenge worldwide, particularly under accelerating climate change. This study assesses spatial and temporal patterns of land degradation and agriculture change in Shaanxi Province, China from 2014 to 2024 by integrating time-series statistical data with remote sensing (RS) techniques. Time series data were used to quantify the total cropland extent, grain, and major crops (wheat and rice) cultivation areas and fertilizer (NPK) consumption; vegetation health and land use land cover (LULC) categories were evaluated to identify dominant drivers and inform sustainable land-management strategies. The results indicated that total cropland area increased by 5.8%, while wheat and rice cultivation areas decreased by 5.9% and 3.2%, respectively, during the study period. Furthermore, RS data shows NDVI median values improving slightly from 0.39 in 2019 to 0.44 in 2024, suggesting gradual recovery of vegetation cover under changing climatic conditions. LULC results revealed minor yet reliable transformations, with cropland showing a modest increase of 4.7% from 2014 to 2024, indicating agricultural stability, rather than large-scale land conversion. Additionally, NPK fertilizer consumption showed a general decrease, reflecting improved input efficiency. Novelty of this research lies in the synchronized integration of long-term statistical record with multi-temporal RS indicators to jointly quantify land use dynamics, vegetation recovery and fertilizer use efficiency at provincial level. Overall, integrating satellite-based and statistical data provided a comprehensive understanding of agricultural dynamics, highlighting the interrelation between land cover change, vegetation condition, and input management practices in Shaanxi Province.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"4 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate change poses a critical threat to Pakistan's land resources, with forest area and carbon stocks serving as key mitigation measures to reduce land degradation. This study introduces an innovative approach to examine the heterogeneous, quantile-dependent relationships among climate change, climate change mitigation measured by forest area and carbon stocks in forests, and their impact on land degradation in Pakistan over the period 1990Q1 to 2022Q4. Employing Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) and Quantile Causality Analysis, the findings reveal that climate change consistently exacerbates land degradation across all quantiles. Both mitigation measures significantly reduce degradation, with forest area exhibiting a broader influence across quantiles and carbon stocks showing particularly strong effects in mid-to-high degradation contexts. Quantile-causality analysis confirms strong predictive effects of climate change at lower-to-mid degradation levels, while mitigation variables demonstrate greater predictive strength in mid-to-high degradation levels. These results emphasize the asymmetric and context-specific nature of the climate change, climate mitigation–land degradation nexus and highlight the value of quantile-based approaches for effective sustainability policy design in environmentally vulnerable economies like Pakistan.
{"title":"How Climate Change and Climate Mitigation Respond to Land Degradation: Novel Insights for Sustainable Land Management in Pakistan","authors":"Anwar Khan, Sami Ullah","doi":"10.1002/ldr.70520","DOIUrl":"https://doi.org/10.1002/ldr.70520","url":null,"abstract":"Climate change poses a critical threat to Pakistan's land resources, with forest area and carbon stocks serving as key mitigation measures to reduce land degradation. This study introduces an innovative approach to examine the heterogeneous, quantile-dependent relationships among climate change, climate change mitigation measured by forest area and carbon stocks in forests, and their impact on land degradation in Pakistan over the period 1990Q1 to 2022Q4. Employing Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) and Quantile Causality Analysis, the findings reveal that climate change consistently exacerbates land degradation across all quantiles. Both mitigation measures significantly reduce degradation, with forest area exhibiting a broader influence across quantiles and carbon stocks showing particularly strong effects in mid-to-high degradation contexts. Quantile-causality analysis confirms strong predictive effects of climate change at lower-to-mid degradation levels, while mitigation variables demonstrate greater predictive strength in mid-to-high degradation levels. These results emphasize the asymmetric and context-specific nature of the climate change, climate mitigation–land degradation nexus and highlight the value of quantile-based approaches for effective sustainability policy design in environmentally vulnerable economies like Pakistan.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"44 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding how vegetation patterns control gravity erosion, such as avalanches, landslides, and mudflows in slope–gully systems under heavy rainfall, remains a key challenge on the Loess Plateau of China. To address this issue, five 1-h simulated rainfalls were conducted at an intensity of 1.4 mm/min on each experimental plot. The plot had a 3° gentle slope and a 70° gully sidewall, and the plot was covered with vegetation. The experimental results show that high-coverage herbaceous vegetation on the gentle slope effectively reduced avalanche magnitude. The plot with 85% grass coverage had the lowest average avalanche volume at 109.6 cm3, across the five rainfall experiments. Conversely, the excessive restoration of woody vegetation, or planting woody vegetation near the gully shoulder line, markedly increased landslide scale. Across the five rainfalls, the average landslide volume was 1202.7 cm3 in the plot with 85% tree coverage and 983.3 cm3 in the plot with 60% shrub coverage along the gully shoulder line—both nearly triple that of bare land. Mudflow volumes in most of the plots accounted for less than 10% of the total gravity erosion. Avalanche and landslide volumes were significantly correlated with root mass density, silt content, bulk density, and organic matter content, with all correlation coefficients exceeding 0.45. In addition, gravity erosion intensified the water erosion. The volumes of the two erosion processes had correlation coefficients of 0.95 and 0.98 for the bare land and shrubland plots, respectively. Consequently, implementing high-coverage herbaceous vegetation on gentle slope is one of the most effective strategies for mitigating gravity erosion on gully sidewall.
{"title":"How Do Vegetation Patterns Control Gravity Erosion in Slope–Gully Systems Under Heavy Rainfall on the Loess Plateau of China?","authors":"Guang Ran, Xiangzhou Xu, Ying Zhao, Altaf Ali Siyal, Yuanjun Zhu","doi":"10.1002/ldr.70457","DOIUrl":"https://doi.org/10.1002/ldr.70457","url":null,"abstract":"Understanding how vegetation patterns control gravity erosion, such as avalanches, landslides, and mudflows in slope–gully systems under heavy rainfall, remains a key challenge on the Loess Plateau of China. To address this issue, five 1-h simulated rainfalls were conducted at an intensity of 1.4 mm/min on each experimental plot. The plot had a 3° gentle slope and a 70° gully sidewall, and the plot was covered with vegetation. The experimental results show that high-coverage herbaceous vegetation on the gentle slope effectively reduced avalanche magnitude. The plot with 85% grass coverage had the lowest average avalanche volume at 109.6 cm<sup>3</sup>, across the five rainfall experiments. Conversely, the excessive restoration of woody vegetation, or planting woody vegetation near the gully shoulder line, markedly increased landslide scale. Across the five rainfalls, the average landslide volume was 1202.7 cm<sup>3</sup> in the plot with 85% tree coverage and 983.3 cm<sup>3</sup> in the plot with 60% shrub coverage along the gully shoulder line—both nearly triple that of bare land. Mudflow volumes in most of the plots accounted for less than 10% of the total gravity erosion. Avalanche and landslide volumes were significantly correlated with root mass density, silt content, bulk density, and organic matter content, with all correlation coefficients exceeding 0.45. In addition, gravity erosion intensified the water erosion. The volumes of the two erosion processes had correlation coefficients of 0.95 and 0.98 for the bare land and shrubland plots, respectively. Consequently, implementing high-coverage herbaceous vegetation on gentle slope is one of the most effective strategies for mitigating gravity erosion on gully sidewall.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"127 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crop and tree compartments cohabit side by side within agroforestry and thus it can possibly induce inherent environmental spatial heterogeneity of soil organic carbon (SOC) and nutrient stocks. Although previous studies largely focused on elucidating agroforestry effects on SOC stocks on spatial scales, its effect on the spatial heterogeneity of nutrient stocks is lacking. Furthermore, little is known about the contrasting effects of short- (≤ 10 years) and long-rotation (≥ 20 years) agroforestry trees on CO2 sequestration rate, and SOC and nutrient stocks accrual, nor is there any robust comparison. This study evaluated four contrasting AFS (IG-AF, IJ-AF, MN-AF, and TK-AF-based) and one adjacent conventional cropland (CL) without trees to elucidate the spatial heterogeneity of SOC concentration, phyto-availability of nutrients and their stocks, and to quantify SOC stocks in the tree biomass, CO2 equivalent sequestration rate in soil and biomass. We also propose the measurement of net carbon sequestration of a given AFS. Random cores were collected from topsoil (0–15 cm) and subsoil (15–30 cm) in quintuples from RS and CS of the AFS along with sampling from CL. Allometrics were used for measuring C stocks in the tree biomass. The magnitude of the measured soil properties (pH, POX-C, SOC, and nutrient concentration) and SOC and nutrient stocks were generally higher (p < 0.05) at RS than CS. Long-rotation TK-AF registered significantly (p < 0.001) the highest concentration of SOC (9.4 and 8.2 g kg−1), POX-C (315.2 and 241.8 mg kg−1), and N (166.1 and 129.4 kg ha−1), P (21.0 and 17.4 kg ha−1), and K (253.0 and 250.2 kg ha−1) availability at RS and CS, respectively, and it also showed the highest TBCS (91.9 Mg ha−1) and NCS (109.1 Mg ha−1). Conversely, short-rotation MN-AF registered significantly the highest CO2–et both in soil (5.4 Mg CO2 ha−1 y−1) and biomass (41.6 Mg CO2 ha−1 y−1). The SOC stock gain over the cropland (ΔSOC) increased with stand age, with the highest gain noticed at IG-AF (17.8 Mg ha−1) followed by TK-AF (17.1 Mg ha−1). Our results warrant long-term experimental agroforestry for a higher elevation of SOC stocks with possibly a steady CO2 sequestration rate to restore the degraded lands in a semiarid environment.
在农林业中，作物隔间和树木隔间共存，可能导致土壤有机碳和养分储量的内在环境空间异质性。虽然以往的研究主要集中在阐明农林业对土壤有机碳储量的空间影响，但其对土壤养分储量空间异质性的影响较少。此外，关于短轮伐（≤10年）和长轮伐（≥20年）农林业树木对CO2固存率、有机碳和养分累积的影响对比研究很少，也没有可靠的比较。本研究通过对4种不同类型AFS （IG-AF、IJ-AF、MN-AF和tk - af）与相邻无树常规农田（CL）的对比分析，揭示了土壤有机碳浓度、养分植物有效性及其储量的空间异质性，并量化了树木生物量中的有机碳储量、土壤和生物量中的二氧化碳当量固存率。我们还建议测量给定AFS的净固碳量。在AFS的RS和CS中随机抽取表土（0-15 cm）和底土（15-30 cm）的五组岩心，并从CL中取样。采用异速测量法测定树木生物量中的碳储量。土壤性质（pH、POX-C、有机碳和养分浓度）、有机碳和养分储量在RS处理下总体高于CS处理（p < 0.05）。长轮作TK-AF在旱作和旱作条件下分别具有最高的有机碳（9.4和8.2 g kg - 1）、POX-C（315.2和241.8 mg kg - 1）、氮（166.1和129.4 kg ha - 1）、磷（21.0和17.4 kg ha - 1）和钾（253.0和250.2 kg ha - 1）有效性（p < 0.001）， TBCS （91.9 mg ha - 1）和NCS （109.1 mg ha - 1）。相反，短轮作MN-AF的土壤CO2 - et （5.4 Mg CO2 ha - 1 y - 1）和生物量（41.6 Mg CO2 ha - 1 y - 1）均最高。随着林龄的增长，土壤有机碳蓄积量（ΔSOC）呈上升趋势，其中IG-AF的最高（17.8 Mg ha - 1），其次是TK-AF （17.1 Mg ha - 1）。我们的研究结果表明，在半干旱环境下，长期的农林业试验可以提高土壤有机碳储量，并可能保持稳定的二氧化碳固存率，以恢复退化的土地。
{"title":"Restoration of Degraded Lands Through Agroforestry: Impact of Trees on Soil Organic Carbon and Nutrient Stocks in Central India","authors":"Sovan Debnath, Sharwan Lal Yadav, Bharti, Bijoy Chanda, Suresh Ramanan S., Asha Ram, Sushil Kumar, Naresh Kumar, Badre Alam, Rajendra Prasad, Tufleuddin Biswas, Ayyanadar Arunachalam","doi":"10.1002/ldr.70508","DOIUrl":"https://doi.org/10.1002/ldr.70508","url":null,"abstract":"Crop and tree compartments cohabit side by side within agroforestry and thus it can possibly induce inherent environmental spatial heterogeneity of soil organic carbon (SOC) and nutrient stocks. Although previous studies largely focused on elucidating agroforestry effects on SOC stocks on spatial scales, its effect on the spatial heterogeneity of nutrient stocks is lacking. Furthermore, little is known about the contrasting effects of short- (≤ 10 years) and long-rotation (≥ 20 years) agroforestry trees on CO<sub>2</sub> sequestration rate, and SOC and nutrient stocks accrual, nor is there any robust comparison. This study evaluated four contrasting AFS (IG-AF, IJ-AF, MN-AF, and TK-AF-based) and one adjacent conventional cropland (CL) without trees to elucidate the spatial heterogeneity of SOC concentration, phyto-availability of nutrients and their stocks, and to quantify SOC stocks in the tree biomass, CO<sub>2</sub> equivalent sequestration rate in soil and biomass. We also propose the measurement of net carbon sequestration of a given AFS. Random cores were collected from topsoil (0–15 cm) and subsoil (15–30 cm) in quintuples from RS and CS of the AFS along with sampling from CL. Allometrics were used for measuring C stocks in the tree biomass. The magnitude of the measured soil properties (pH, POX-C, SOC, and nutrient concentration) and SOC and nutrient stocks were generally higher (<i>p</i> < 0.05) at RS than CS. Long-rotation TK-AF registered significantly (<i>p</i> < 0.001) the highest concentration of SOC (9.4 and 8.2 g kg<sup>−1</sup>), POX-C (315.2 and 241.8 mg kg<sup>−1</sup>), and N (166.1 and 129.4 kg ha<sup>−1</sup>), P (21.0 and 17.4 kg ha<sup>−1</sup>), and K (253.0 and 250.2 kg ha<sup>−1</sup>) availability at RS and CS, respectively, and it also showed the highest TBCS (91.9 Mg ha<sup>−1</sup>) and NCS (109.1 Mg ha<sup>−1</sup>). Conversely, short-rotation MN-AF registered significantly the highest CO<sub>2</sub>–<i>e</i><sub><i>t</i></sub> both in soil (5.4 Mg CO<sub>2</sub> ha<sup>−1</sup> y<sup>−1</sup>) and biomass (41.6 Mg CO<sub>2</sub> ha<sup>−1</sup> y<sup>−1</sup>). The SOC stock gain over the cropland (Δ<sub>SOC</sub>) increased with stand age, with the highest gain noticed at IG-AF (17.8 Mg ha<sup>−1</sup>) followed by TK-AF (17.1 Mg ha<sup>−1</sup>). Our results warrant long-term experimental agroforestry for a higher elevation of SOC stocks with possibly a steady CO<sub>2</sub> sequestration rate to restore the degraded lands in a semiarid environment.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"39 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melissa Alexandre Santos, Borja Velázquez-Martí, Jorge João Delfim, Laura Silva Nantes, Jose Augusto Liberato de Souza, Gabriel Augusto da Silva Lunardelli, Dayara Vivian Alvares, Carolina dos Santos Batista Bonini
This study presents an innovative assessment model for analyzing the evolution of degraded soils subjected to different reclamation strategies. The proposal combines statistical and artificial intelligence tools to jointly integrate multiple physical and chemical soil properties, allowing for a more synthetic view of the processes. The model uses principal component analysis to synthesize information on the most relevant variables and subsequently applies probabilistic neural networks to identify the most likely values of the principal components when a given soil reclamation treatment is applied. Once the optimal ranges for a successfully reclaimed soil have been defined, the developed numerical methods are applied, defining an area considered optimal in the principal component diagram. The most appropriate treatment is considered to be the one most likely to occupy the optimal area. The methodology was applied to a dystrophic Red Oxisol degraded by the construction of the Ilha Solteira Hydroelectric Plant in Brazil, where a long-term experiment with two tree species (Eucalyptus urograndis and Mabea fistulifera) and different doses of organic and mineral fertilization was established in 2010. The results show that the combination of M. fistulifera with the application of 20 Mg·ha−1 of compost significantly improves organic matter, porosity, and cation exchange capacity in the surface soil horizons, generating more favorable conditions for plant growth in the long term. Beyond the specific results, this multivariate model represents a useful tool for evaluating the effectiveness of long-term soil restoration programs, providing objective criteria that can guide decision-making in projects for ecological recovery and sustainable management of degraded lands.
{"title":"Application of Principal Component Analysis and Probabilistic Neural Networks in Ferralsols Recovery Evaluation Through Planting of Mabea Fistulifera and Eucalyptus Urograndis","authors":"Melissa Alexandre Santos, Borja Velázquez-Martí, Jorge João Delfim, Laura Silva Nantes, Jose Augusto Liberato de Souza, Gabriel Augusto da Silva Lunardelli, Dayara Vivian Alvares, Carolina dos Santos Batista Bonini","doi":"10.1002/ldr.70465","DOIUrl":"https://doi.org/10.1002/ldr.70465","url":null,"abstract":"This study presents an innovative assessment model for analyzing the evolution of degraded soils subjected to different reclamation strategies. The proposal combines statistical and artificial intelligence tools to jointly integrate multiple physical and chemical soil properties, allowing for a more synthetic view of the processes. The model uses principal component analysis to synthesize information on the most relevant variables and subsequently applies probabilistic neural networks to identify the most likely values of the principal components when a given soil reclamation treatment is applied. Once the optimal ranges for a successfully reclaimed soil have been defined, the developed numerical methods are applied, defining an area considered optimal in the principal component diagram. The most appropriate treatment is considered to be the one most likely to occupy the optimal area. The methodology was applied to a dystrophic Red Oxisol degraded by the construction of the Ilha Solteira Hydroelectric Plant in Brazil, where a long-term experiment with two tree species (<i>Eucalyptus urograndis</i> and <i>Mabea fistulifera</i>) and different doses of organic and mineral fertilization was established in 2010. The results show that the combination of <i>M. fistulifera</i> with the application of 20 Mg·ha<sup>−1</sup> of compost significantly improves organic matter, porosity, and cation exchange capacity in the surface soil horizons, generating more favorable conditions for plant growth in the long term. Beyond the specific results, this multivariate model represents a useful tool for evaluating the effectiveness of long-term soil restoration programs, providing objective criteria that can guide decision-making in projects for ecological recovery and sustainable management of degraded lands.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"49 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rice paddies are a major source of methane (CH4), and effective mitigation strategies are urgently needed. Biochar has been proposed as a promising option; however, quantitative effects on CH4-cycling microbial processes and their underlying mechanisms remain unclear. Here, we conducted a meta-analysis and a random-effects model to evaluate the effects of biochar on CH4 emissions, crop yield, and key microbial functional genes and soil properties, drawing primarily on studies conducted in China. Overall, biochar application reduced CH4 emissions by 26.4% and increased rice yield by 6.2%. These responses were associated with enhanced plant biomass, which suppressed methanogen activity while stimulating methanotrophs, likely mediated by increased root oxygen release and rhizosphere carbon availability. Notably, the decreased mcrA/pmoA ratio highlighted a shift in microbial functional balance favoring CH4 mitigation. Our study provides a more comprehensive synthesis by linking biochar-induced changes in microbial functional genes to CH4 mitigation and crop productivity. These findings offer quantitative evidence and practical guidance for biochar application in climate-smart and sustainable rice cultivation.
{"title":"The Positive Effect of Biochar on Rice Growth Increases Methanotrophs to Mitigate Methane Emissions: A Meta-Analysis","authors":"Zhiwei Zhang, Mingwang Lu, Xiaomeng Bo, Shumin Guo, Mengxue Shen, Jinyang Wang, Jianwen Zou","doi":"10.1002/ldr.70500","DOIUrl":"https://doi.org/10.1002/ldr.70500","url":null,"abstract":"Rice paddies are a major source of methane (CH<sub>4</sub>), and effective mitigation strategies are urgently needed. Biochar has been proposed as a promising option; however, quantitative effects on CH<sub>4</sub>-cycling microbial processes and their underlying mechanisms remain unclear. Here, we conducted a meta-analysis and a random-effects model to evaluate the effects of biochar on CH<sub>4</sub> emissions, crop yield, and key microbial functional genes and soil properties, drawing primarily on studies conducted in China. Overall, biochar application reduced CH<sub>4</sub> emissions by 26.4% and increased rice yield by 6.2%. These responses were associated with enhanced plant biomass, which suppressed methanogen activity while stimulating methanotrophs, likely mediated by increased root oxygen release and rhizosphere carbon availability. Notably, the decreased <i>mcrA</i>/<i>pmoA</i> ratio highlighted a shift in microbial functional balance favoring CH<sub>4</sub> mitigation. Our study provides a more comprehensive synthesis by linking biochar-induced changes in microbial functional genes to CH<sub>4</sub> mitigation and crop productivity. These findings offer quantitative evidence and practical guidance for biochar application in climate-smart and sustainable rice cultivation.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"46 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147361011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Da Zhang, Fengru Yang, Ying Nan, Hengdong Feng, Licheng Peng, Manyu Cui
Analyzing the impacts of urban expansion on natural habitats and endangered species is important in protecting biodiversity. However, few studies have assessed the indirect impacts of urban expansion on natural habitats as well as endangered species. In this study, the impacts of cropland displacement triggered by urban expansion on natural habitats and endangered species are defined as indirect impacts. We took the Japan Sea Rim (JSR) region as study area, simulated future cropland displacement based on net primary productivity (NPP) data using the zoned Land Use Scenario Dynamics-urban (LUSD-urban) model, and evaluated the direct and indirect impacts of urban expansion on natural habitats as well as endangered species at the whole JSR scale and national scale. The results indicated that during 1992–2020 natural habitat has lost 5575 km2 due to direct encroachment of urban expansion. During 2020–2050, a direct decline of 1232–2793 km2 of natural habitats is predicted under the influence of future urban expansion. Comparing with direct loss, an indirect decline of 6855–13,484 km2 of natural habitats is predicted caused by future cropland displacement. The indirect loss of natural habitat in 2020–2050 will be 4.83–5.56 times more than the direct loss. Meanwhile, for endangered species, urban expansion will directly affect 833–837 endangered species in 2020–2050, while cropland displacement will affect 923 endangered species during the same period. The indirect impact on endangered species will be 1.10–1.11 times more than the direct impact. Policies and measures should be enacted to balance urban expansion, cropland displacement, and natural habitat conservation in the JSR.
{"title":"The Impacts of Urban Expansion on Natural Habitats and Endangered Species in the Japan Sea Rim Region","authors":"Da Zhang, Fengru Yang, Ying Nan, Hengdong Feng, Licheng Peng, Manyu Cui","doi":"10.1002/ldr.70491","DOIUrl":"https://doi.org/10.1002/ldr.70491","url":null,"abstract":"Analyzing the impacts of urban expansion on natural habitats and endangered species is important in protecting biodiversity. However, few studies have assessed the indirect impacts of urban expansion on natural habitats as well as endangered species. In this study, the impacts of cropland displacement triggered by urban expansion on natural habitats and endangered species are defined as indirect impacts. We took the Japan Sea Rim (JSR) region as study area, simulated future cropland displacement based on net primary productivity (NPP) data using the zoned Land Use Scenario Dynamics-urban (LUSD-urban) model, and evaluated the direct and indirect impacts of urban expansion on natural habitats as well as endangered species at the whole JSR scale and national scale. The results indicated that during 1992–2020 natural habitat has lost 5575 km<sup>2</sup> due to direct encroachment of urban expansion. During 2020–2050, a direct decline of 1232–2793 km<sup>2</sup> of natural habitats is predicted under the influence of future urban expansion. Comparing with direct loss, an indirect decline of 6855–13,484 km<sup>2</sup> of natural habitats is predicted caused by future cropland displacement. The indirect loss of natural habitat in 2020–2050 will be 4.83–5.56 times more than the direct loss. Meanwhile, for endangered species, urban expansion will directly affect 833–837 endangered species in 2020–2050, while cropland displacement will affect 923 endangered species during the same period. The indirect impact on endangered species will be 1.10–1.11 times more than the direct impact. Policies and measures should be enacted to balance urban expansion, cropland displacement, and natural habitat conservation in the JSR.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"31 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duanchen Zhang, Yan Xu, Huarui Gong, Jing Li, Xubo Zhang
Salinization in coastal deltas threatens food security for nearly 10% of the global population. Accurate assessment of its soil quality, coupled with characterization of long-term spatiotemporal dynamics and driving mechanisms, provides the scientific basis for developing soil amelioration strategies and promoting agricultural productivity. However, conventional evaluation frameworks often overlook the unique pedological characteristics of coastal saline-alkaline environments. This study focused on the Yellow River Delta's coastal saline-alkali farmlands, integrating 8856 multi-source data points—including soil, groundwater, climate, and management variables—collected during the 2010s and 2020s across 8000 km of field surveys. We developed a scalable, region-specific framework for coastal delta soil quality assessment, integrating a Principal Component Analysis (PCA)-based Minimum Data Set (MDS), a fuzzy logic soil quality index (SQI), and an interpretable machine learning model using XGBoost with SHapley values. Results indicate SQI values increased from 0.19–0.80 in the 2010s to 0.27–0.81 in the 2020s. Fluctuations in groundwater depth (40.8%), changes in nitrogen fertilizer input (30.7%), distance to the Yellow River (12.6%), and variations in the Standardized Precipitation Evapotranspiration Index (11.2%) were identified as the primary drivers of SQI improvement. A temporal shift was observed from a fertilizer-dominated (2010s) to a comprehensive water-regulated (2020s) regime driven by groundwater, irrigation, and precipitation dynamics, underscoring the growing influence of integrated water management. These findings highlight the critical role of integrated water-fertilizer management and call for targeted strategies, including enhanced groundwater monitoring and precision irrigation. Our framework offers a scalable approach for tracking and improving soil quality in salt-affected coastal agroecosystems worldwide.
{"title":"From Fertilizer to Water: A Decadal Shift in Soil Quality Drivers in Salt-Affected Coastal Deltas","authors":"Duanchen Zhang, Yan Xu, Huarui Gong, Jing Li, Xubo Zhang","doi":"10.1002/ldr.70527","DOIUrl":"https://doi.org/10.1002/ldr.70527","url":null,"abstract":"Salinization in coastal deltas threatens food security for nearly 10% of the global population. Accurate assessment of its soil quality, coupled with characterization of long-term spatiotemporal dynamics and driving mechanisms, provides the scientific basis for developing soil amelioration strategies and promoting agricultural productivity. However, conventional evaluation frameworks often overlook the unique pedological characteristics of coastal saline-alkaline environments. This study focused on the Yellow River Delta's coastal saline-alkali farmlands, integrating 8856 multi-source data points—including soil, groundwater, climate, and management variables—collected during the 2010s and 2020s across 8000 km of field surveys. We developed a scalable, region-specific framework for coastal delta soil quality assessment, integrating a Principal Component Analysis (PCA)-based Minimum Data Set (MDS), a fuzzy logic soil quality index (SQI), and an interpretable machine learning model using XGBoost with SHapley values. Results indicate SQI values increased from 0.19–0.80 in the 2010s to 0.27–0.81 in the 2020s. Fluctuations in groundwater depth (40.8%), changes in nitrogen fertilizer input (30.7%), distance to the Yellow River (12.6%), and variations in the Standardized Precipitation Evapotranspiration Index (11.2%) were identified as the primary drivers of SQI improvement. A temporal shift was observed from a fertilizer-dominated (2010s) to a comprehensive water-regulated (2020s) regime driven by groundwater, irrigation, and precipitation dynamics, underscoring the growing influence of integrated water management. These findings highlight the critical role of integrated water-fertilizer management and call for targeted strategies, including enhanced groundwater monitoring and precision irrigation. Our framework offers a scalable approach for tracking and improving soil quality in salt-affected coastal agroecosystems worldwide.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"86 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147329861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low-concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X-ray fluorescence (pXRF) sensors, aiming to address the limitations of traditional single sensors in predicting low-concentration heavy metals in arid farmland areas. Using 116 soil samples from Qapqal County, this study screened 225 preprocessing combinations and identified Vis–NIR with 1.75-order differentiation as the most effective approach for predicting heavy metals. It achieved high prediction accuracy with R2 values of 0.71 (As), 0.68 (Pb), 0.64 (Cd), and 0.50 (Cu), clearly outperforming pXRF. The best performance of pXRF was achieved with standard normal variate transformation preprocessing, yet its accuracy remained considerably low—for instance, the R2 for Cd was only 0.29. Moreover, its accuracy further decreased under fractional-order differentiation, indicating that fractional-order differentiation preprocessing is unsuitable for pXRF. The model accuracy was significantly improved by employing differentiated spectral preprocessing combinations, particularly for As, with an R2 of 0.72, LCCC of 0.76, and RPIQ of 3.27. Furthermore, the analysis of critical characteristic bands revealed that the characteristic bands of As, Pb, and Cu are mainly concentrated in the low-energy region (5–16 keV) of pXRF, providing an essential spectral basis for heavy metal feature extraction. This study innovatively proposes differentiated preprocessing strategies and highlights the critical role of pXRF low-energy region spectra in heavy metal prediction. The research provides a scientific basis for heavy metal monitoring and ecological risk assessment of farmland in arid areas, which has significant practical value, contributing to improved environmental quality and the safety of agricultural products.
{"title":"Fractional Order Differentiation Preprocessing Based Fusion of Vis–NIR and pXRF: A New Strategy for Precise Monitoring of Low Concentration Heavy Metals in Arid Agricultural Soils","authors":"Liangyi Li, Zipeng Zhang, Jianli Ding, Yuxin Ren, Jinhua Cao, Keqiang Wang, Jiayu Li, Chuanmei Zhu, Xiangyu Ge, Jinjie Wang, Xiang Li, Chaolei Yang, Jingzhe Wang","doi":"10.1002/ldr.70525","DOIUrl":"https://doi.org/10.1002/ldr.70525","url":null,"abstract":"Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low-concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X-ray fluorescence (pXRF) sensors, aiming to address the limitations of traditional single sensors in predicting low-concentration heavy metals in arid farmland areas. Using 116 soil samples from Qapqal County, this study screened 225 preprocessing combinations and identified Vis–NIR with 1.75-order differentiation as the most effective approach for predicting heavy metals. It achieved high prediction accuracy with <i>R</i><sup>2</sup> values of 0.71 (As), 0.68 (Pb), 0.64 (Cd), and 0.50 (Cu), clearly outperforming pXRF. The best performance of pXRF was achieved with standard normal variate transformation preprocessing, yet its accuracy remained considerably low—for instance, the <i>R</i><sup>2</sup> for Cd was only 0.29. Moreover, its accuracy further decreased under fractional-order differentiation, indicating that fractional-order differentiation preprocessing is unsuitable for pXRF. The model accuracy was significantly improved by employing differentiated spectral preprocessing combinations, particularly for As, with an <i>R</i><sup>2</sup> of 0.72, LCCC of 0.76, and RPIQ of 3.27. Furthermore, the analysis of critical characteristic bands revealed that the characteristic bands of As, Pb, and Cu are mainly concentrated in the low-energy region (5–16 keV) of pXRF, providing an essential spectral basis for heavy metal feature extraction. This study innovatively proposes differentiated preprocessing strategies and highlights the critical role of pXRF low-energy region spectra in heavy metal prediction. The research provides a scientific basis for heavy metal monitoring and ecological risk assessment of farmland in arid areas, which has significant practical value, contributing to improved environmental quality and the safety of agricultural products.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"25 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147329862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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